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8th International Conference on swarm Intelligence (ICSI)

作者： Hussain, Kashif Salleh, Mohd Najib Mohd Cheng, Shi Shi, Yuhui Univ Tun Hussein Onn Malaysia Batu Pahat Malaysia Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9783319618241;9783319618234

swarm-based metaheuristic algorithms inspired from swarm systems in nature have produced remarkable results while solving complex optimization problems. This is due to their capability of decentralized control of search agents able to explore search environment more effectively. The large number of metaheuristics sometimes puzzle beginners and practitioners where to start with. This experimental study covers 10 swarm-based metaheuristic algorithms introduced in last decade to be investigated on their performances on 12 test functions of high dimensions with diverse features of modality, scalability, and valley landscape. based on simulations, it can be concluded that firefly algorithm outperformed rest of the algorithms while tested unimodal functions. On multimodal functions, animal migration algorithm produced outstanding results as compared to rest of the methods. In future, further investigation can be conducted on relating benchmark functions to real-world optimization problem so that metaheuristic algorithms can be grouped according to suitability of problem characteristics.

关键词： swarm-intelligence swarm-based algorithms Metaheuristic Benchmark functions

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Routing algorithm for sparse unstructured P2P networks using honey bee behavior

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第2期38卷

作者： Verma, Aman Thakur, Sanat Kumar, Ankush Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 Himachal Prades India

Unstructured peer-to-peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps. We worked on the scalable and efficient routing in unstructured network. We used the Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P ***

关键词： bee routing algorithm comparative analysis intelligent routing optimization swarm-based algorithms

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Bees Algorithm for multimodal function optimisation

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2016年第5期230卷 867-884页

作者： Zhou, Z. D. Xie, Y. Q. Pham, D. T. Kamsani, S. Castellani, M. Wuhan Univ Technol Sch Informat Engn Luoshi Rd 122 Wuhan 430070 Peoples R China Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

The aim of multimodal optimisation is to find significant optima of a multimodal objective function including its global optimum. Many real-world applications are multimodal optimisation problems requiring multiple optimal solutions. The Bees Algorithm is a global optimisation procedure inspired by the foraging behaviour of honeybees. In this paper, several procedures are introduced to enhance the algorithm's capability to find multiple optima in multimodal optimisation problems. In the proposed Bees Algorithm for multimodal optimisation, dynamic colony size is permitted to automatically adapt the search effort to different objective functions. A local search approach called balanced search technique is also proposed to speed up the algorithm. In addition, two procedures of radius estimation and optima elitism are added, to respectively enhance the Bees Algorithm's ability to locate unevenly distributed optima, and eliminate insignificant local optima. The performance of the modified Bees Algorithm is evaluated on well-known benchmark problems, and the results are compared with those obtained by several other state-of-the-art algorithms. The results indicate that the proposed algorithm inherits excellent properties from the standard Bees Algorithm, obtaining notable efficiency for solving multimodal optimisation problems due to the introduced modifications.

关键词： swarm-based algorithms multimodal optimisation Bees Algorithm balanced search technique hill valley

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Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 10733-10749页

作者： Shehadeh, Hisham A. Amman Arab Univ Fac Comp Sci & Informat Dept Comp Informat Syst Amman Jordan Amman Arab Univ Fac Comp Sci & Informat Dept Comp Sci Amman Jordan

This paper proposes a novel meta-heuristic optimization method, namely ''Chernobyl Disaster Optimizer (CDO)''. The underlying concepts and principles behind the proposed approach is inspired by the nuclear reactor core explosion of Chernobyl. In CDO, radioactivity happened because of nuclear instability, which different types of radiations are emitted from nuclei. The most common kinds of these radiations are called gamma, beta, and alpha particles. These particles fly away from the explosion point (high pressure point) to the low pressure point (the human standing point), which are harmful to the humans. The CDO mimics the process of nuclear radiation while attaching human after the nuclear explosion. The main steps of nuclear explosion and attaching human are implemented in which gamma, beta, and alpha particles are involved in this process. The CDO is evaluated with optimizing ''Congress on Evolutionary Computation (CEC 2017)'' test bed suites. In addition, it is compared against well-known optimization methods, such as ''Sperm swarm Optimization'' and ''Gravitational Search Algorithm''. The experimental results prove its efficiency, which can be considered as viable alternative.

关键词： Physical-based algorithms swarm-based algorithms Sperm swarm optimization (SSO) Gravitational search algorithm (GSA) (CEC) 2017 benchmarks functions

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A novel upgraded bat algorithm based on cuckoo search and Sugeno inertia weight for large scale and constrained engineering design optimization problems

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1731-1758页

作者： Pathak, Vimal Kumar Srivastava, Ashish Kumar Manipal Univ Jaipur Dept Mech Engn Jaipur 303007 Rajasthan India

The bat algorithm (BA) is one of prominent swarm-based algorithm that has the suitability in solving only small dimension engineering problems and suffers from drawback of getting trapped in local minimum with slow convergence for multi-dimensional problems. In the context of improving its applicability in solving large scale and constrained engineering design problems, this paper presents a novel upgraded bat algorithm with cuckoo search and Sugeno inertia weight (UBCSIW). In the proposed UBCSIW algorithm, first, the bat algorithm with its competence to exploit the optimal solutions in search space is combined with cuckoo search with its ability to explore best solution globally using Levy flight in the search space. Secondly, a new velocity and position search equation is incorporated in which the bat searches around the best candidate solution. This step helps in establishing adequate balance between exploration and exploitation capability and improving the performance effectively by employing greedy selection to choose the best candidate solution. Finally, Sugeno fuzzy inertia weight is introduced in the velocity updation equation, boosting the flexibility and diversity of bat population and results in stability of results. The effectiveness of the proposed UBCSIW algorithm is tested on 16 standard benchmark functions (unimodal and multimodal) with different dimensions, 12 CEC2015 test functions and 7 well-known constrained engineering design problems. The outputs of the proposed UBCSIW algorithm are validated by comparison with classical BA and other swarm-based state-of-the art algorithms. The simulation results show that proposed UBCSIW algorithm achieves highly competitive results in terms of higher optimization accuracy and improved convergence that outperforms basic BA in all twenty-eight test functions while performs better than other competitive algorithms in 24 functions (13 benchmark and 11 CEC2015 functions).

关键词： Bat algorithm swarm-based algorithms Constrained optimization Large scale problems

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A hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA) for global optimization

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NEURAL COMPUTING & APPLICATIONS 2021年第18期33卷 11739-11752页

作者： Shehadeh, Hisham A. Amman Arab Univ Dept Comp Syst & Technol Fac Comp Sci & Informat Amman Jordan Amman Arab Univ Dept Comp Sci Fac Comp Sci & Informat Amman Jordan

This paper proposes a new hybrid optimization algorithm, called "(HSSOGSA)" with the combination of "gravitational search algorithm (GSA)" and "sperm swarm optimization (SSO)". The underlying concepts and ideas behind the proposed algorithm are to combine the capability of exploitation in SSO with the capability of exploration in GSA to synthesize both algorithms' strength. To evaluate the efficiency of the proposed approach, different test bed problems of optimization are considered, called the "congress on evolutionary computation (CEC)" 2017 suite. The proposed HSSOGSA is compared against both the standard GSA and SSO algorithms. These algorithms are compared based on two mechanisms, including, qualitative and quantitative tests. For the quantitative test, we adopt best fitness, standard deviation, and average measures, while for the qualitative test, we compare between the convergence rates achieved by the proposed algorithm and the convergence rates achieved by SSO and GSA. The outcomes of the study present the hybrid method possesses a better capability and performance to escape from local extremes with faster rate of convergence than the standard SSO and GSA for the majority of benchmarks functions of wide and narrow search space domain.

关键词： swarm-based algorithms Physical-based algorithms Sperm swarm optimization (SSO) Gravitational search algorithm (GSA) Hybrid algorithm (CEC) 2017 benchmarks functions

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Evaluating Performance of Honey Bee Mating Optimization

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2014年第3期160卷 1020-1026页

作者： Karimi, Somayeh Mostoufi, Navid Sotudeh-Gharebagh, Rahmat Univ Tehran Sch Chem Engn Coll Engn Proc Design & Simulat Res Ctr Tehran Iran

The honey bee mating optimization (HBMO) algorithm is presented and tested with various test functions, and its performance is compared with the genetic algorithm (GA). It is shown that the HBMO algorithm can overcome the weaknesses of the GA. The HBMO converges faster than the GA. Even when the HMBO starts from a more improper initial condition than the GA, it can reach a better solution in a smaller number of function evaluations. Furthermore, in some cases, the GA was not able to reach the global minimum.

关键词： Honey bees mating optimization swarm-based algorithms swarm intelligence

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swarm-Inspired Routing algorithms for Unstructured P2P Networks

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INTERNATIONAL JOURNAL OF swarm INTELLIGENCE RESEARCH 2018年第3期9卷 23-63页

作者： Sesum-Cavic, Vesna Kuehn, Eva Zischka, Stefan Vienna Univ Technol Inst Comp Languages Space Based Comp Grp Vienna Austria Vienna Univ Technol Vienna Austria

Due to extreme complexity in nowadays networks, routing becomes a challenging task. This problem is especially delicate in unstructured P2P networks, as there is neither a global view on the network nor a global address mapping. Although different conventional solutions are commercially available, swarm-intelligent approaches are promising in case of frequently changing conditions in P2P networks. In this article, an approach inspired by Dictyostelium discoideum slime molds and bees with distributive and autonomous properties is proposed. Both bio-mechanisms are "tailored" for routing in unstructured P2P systems, resulting in swarm-inspired routing algorithms, SMNet and BeeNet. They are compared with three swarm-based routing algorithms and two conventional approaches. The benchmarks include parameter sensitivity -, comparative -, statistical - and scalability-analysis. SMNet outperforms the other algorithms in the comparative analysis regarding the average data packet delay, especially for bigger network sizes and data packet traffic levels. Both algorithms show good scalability.

关键词： Bee Routing Algorithm Comparative Analysis Intelligent Routing Parameters Tuning Scalability Analysis Slime Mold Routing Algorithm swarm-based algorithms Unstructured P2P Overlay

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Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm

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WEATHER AND CLIMATE EXTREMES 2023年 41卷

作者： Razavi-Termeh, Seyed Vahid Seo, MyoungBae Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi Sejong Univ XR Res Ctr Dept Comp Sci & Engn & Convergence Engn Intelligen Seoul South Korea Korea Inst Civil Engn & Bldg Technol Future & Smart Construct Div Goyang South Korea

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging opti-mization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas;70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood sus-ceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling ac-curacy than the standalone XGBoost model.

关键词： Monsoon floods Satellite imagery swarm-based algorithms Machine learning Spatial modeling

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Randomized Grey Wolf Optimizer (RGWO) with randomly weighted coefficients 10

Randomized Grey Wolf Optimizer (RGWO) with randomly weighted...

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10th International Conference on Contemporary Computing (IC3)

作者： Jaiswal, Kartikeya Mittal, Himanshu Kukreja, Sonia Jaypee Inst Informat Technol Noida India

ISBN: (纸本)9781538630778

Meta-heuristic algorithms are quiet widely used in engineering and science. In this paper, a novel variant of grey-wolf optimizer, randomized gray-wolf optimizer, is proposed where the effect of random coefficients of the best agents are taken into consideration. The proposed variant is tested on a set of 12 standard benchmark functions and compared with grey-wolf optimizer and particle swarm optimization on different dimensional search spaces. To statistically validate and analyse the convergence behaviour of variant, wilcoxon rank sum test along with convergence graphs over 30 runs is studied. The experimental study signify that the proposed variant not only enhances the performance but has comparable convergence rate, especially in case of multi-modal problems.

关键词： Grey wolf optimizer Randomized Grey wolf optimizer swarm-based algorithms Meta-heuristics Nature-inspired optimisation

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